Identification of arachidonylethanolamide (anandamide) is an endogenous cannabinoid is one of the most important developments in cannabinoid research in recent years. Generally, anandamide and all known analogs exhibit significant selectivity for the CB1 receptor and modest to very low affinity for CB2. Anandamide degradation can be prevented by including phenylmethylsulfonyl fluoride (PMSF), a common esterase and protease non-specific inhibitor, during receptor binding assays. Elucidating how structurally different anandamide analogs and cannabinoid analogs bind to the CB1 receptor has been an important goal. Extensive structure-activity relationship (SAR) studies for cannabinoid compounds have been conducted. In contrast, development of anandamide is relatively recent. SAR profiles have focused on the spatial disposition of proposed pharmacopheric elements. We propose that a reliable quantitative SAR (QSAR) must also include electronic descriptors as well as lipophilicity and solubility. A reliable QSAR will aid in the understanding of anandamide binding to the CB1 receptor and the design of novel drugs that are potentially useful in therapeutical applications. Our approach will combine force-field approaches for conformational searchers, semi-empirical (AM1) and ab initio (Hartree-Frock and B3LYP) quantum chemistry for structural refinement and charge distribution. Molecular descriptors will then be used in regression analysis and neural network approaches to develop QSAR. The main advantage of this approach is that since this model is based on the calculated descriptors, it can be applied to predict the activity of unknown anandamide analogs. The availability of more stable anandamide analogs to amidase hydrolysis will prove useful for future in vitro and in vivo studies aimed at delineating their mode of action in the brain.